All the architectures mentioned for sketch generation in vector format, are VAEs. A well known disadvantage with VAEs, they tend to produce blurred images in case of raster images. Since there is no concept of blurring, the vector images produced by VAEs like sketch-rnn [1]} tend to suffer from a mode-collapse-like situation wherein the pen is not lifted to draw at another location, but stays on the paper and continues to scribble. We call this as the scribble effect. Figure REF  shows this effect in the sketches of “yoga poses" and “mosquitos". Since VAEs assume the prior to be Gaussian, they need to be trained for a very large number of iterations so that the weights of the decoder get adjusted accordingly in order to generate close to the distribution of data. In the case of [1]}, the training is done for 10 million iterations. Also, GANs have performed outstandingly well for a variety of tasks mentioned in Section , with raster images. In order to alleviate these disadvantages of VAEs and harness the power of GANs, we propose a standalone GAN called the SkeGAN and another GAN called the VASkeGAN with which we compare SkeGAN.
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